Preprint Data Descriptor Version 1 Preserved in Portico This version is not peer-reviewed

Prognosease: A Data Generator for Health Deterioration Prognosis

Version 1 : Received: 25 January 2023 / Approved: 26 January 2023 / Online: 26 January 2023 (08:37:30 CET)

A peer-reviewed article of this Preprint also exists.

Berghout, T.; Benbouzid, M. PrognosEase: A Data Generator for Health Deterioration Prognosis. SoftwareX 2023, 101461, doi:10.1016/j.softx.2023.101461. Berghout, T.; Benbouzid, M. PrognosEase: A Data Generator for Health Deterioration Prognosis. SoftwareX 2023, 101461, doi:10.1016/j.softx.2023.101461.

Abstract

This paper presents PrognosEase; a software that provides an easier way to produce different types of run-to-failure data mimicking real-world conditions to simplify prognosis studies in terms of data collection and improvement in ML degradation modelling process. Different types of degradation types made available to meet different types of applications. Besides, some primary ML tests were performed to ensure that complexity patterns of real systems could be observed in the training/testing predictions attitude. This paper also presents the impacts, limitations and potential improvements of the data generator.

Keywords

Data generator; dataset; deep learning; health index; machine learning; prognosis and health management; remaining useful life

Subject

Computer Science and Mathematics, Applied Mathematics

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